TorchServe Server-Side Request Forgery vulnerability
Impact
Remote Server-Side Request Forgery (SSRF)
Issue: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions 0.1.0 to 0.8.1.
Mitigation: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the allowed_urls and specifying the model URL to be used. A pull request to warn the user when the default value for allowed_urls is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release 0.8.2 includes this change.
Patches
TorchServe release 0.8.2 includes fixes to address the previously listed issue:
https://github.com/pytorch/serve/releases/tag/v0.8.2
Tags for upgraded DLC release User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2: x86 GPU
- v1.9-pt-ec2-2.0.1-inf-gpu-py310
- v1.8-pt-sagemaker-2.0.1-inf-gpu-py310
x86 CPU
- v1.8-pt-ec2-2.0.1-inf-cpu-py310
- v1.7-pt-sagemaker-2.0.1-inf-cpu-py310
Graviton
- v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310
- v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310
Neuron
- 1.13.1-neuron-py310-sdk2.13.2-ubuntu20.04
- 1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04
- 1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04
The full DLC image URI details can be found at: https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images
References
https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296 https://github.com/pytorch/serve/pull/2534 https://github.com/pytorch/serve/releases/tag/v0.8.2 https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images
Credit
We would like to thank Oligo Security for responsibly disclosing this issue and working with us on its resolution. If you have any questions or comments about this advisory, we ask that you contact AWS/Amazon Security via our vulnerability reporting page](https://aws.amazon.com/security/vulnerability-reporting)) or directly via email to aws-security@amazon.com. Please do not create a public GitHub issue.
Betroffene Software
Klicken Sie auf eine Technologie, um alle zugehörigen Schwachstellen anzuzeigen.
| Plattform | Paket | Anfällige Versionen | Behoben in |
|---|---|---|---|
| Python (PyPI) | torchserve | >= 0.1.0, < 0.8.2 | 0.8.2 |
Verweise
- https://github.com/pytorch/serve/security/advisories/GHSA-8fxr-qfr9-p34w
- https://nvd.nist.gov/vuln/detail/CVE-2023-43654
- https://github.com/pytorch/serve/pull/2534
- https://github.com/pytorch/serve
- https://github.com/pytorch/serve/releases/tag/v0.8.2
- http://packetstormsecurity.com/files/175095/PyTorch-Model-Server-Registration-Deserialization-Remote-Code-Execution.html
- https://pypi.org/project/torchserve
- https://github.com/advisories/GHSA-8fxr-qfr9-p34w
- CVSS-Vektor
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H- Schwäche (CWE)
CWE-918- Plattform
- Python (PyPI)
- Paket
torchserve- Veröffentlicht
- 02.10.2023
- Aktualisiert
- 11.10.2026 15:17
Quellen und Lizenzen
OSV / GitHub Advisory Database - PYSEC-2026-553 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-8fxr-qfr9-p34w - CC BY 4.0
Schwachstellenhistorie: torchserve
Alle veröffentlichten Schwachstellen dieses Pakets (Python (PyPI)), neueste zuerst. Zeigt, ob es ein einmaliger Ausrutscher ist oder ein Paket, das man im Auge behalten muss.
- Einträge insgesamt
- 6
- Erste → letzte
- 10.2023 03.2025
- Ohne Fix
- 1
- In Angriffen ausgenutzt
- 0
Verteilung nach Schweregrad
- Kritisch 3
- Hoch 1
- Mittel 2
Veröffentlichungen pro Jahr
- 2023: 3
- 2024: 2
- 2025: 1
- 2026: 0
-
TorchServe Server-Side Request Forgery vulnerability dieser Eintrag
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